Deep Learning
Showing 36 of 937 projects
A real-time RGB-based pipeline for object detection and 6D pose estimation using a denoising autoencoder trained on simulated 3D views.
A Python package for training PyTorch neural networks using variational inference for Bayesian deep learning.
TensorFlow implementation of weakly-supervised object localization using only image-level labels, without bounding box annotations.
A BERT-based foundation model pretrained on large-scale scRNA-seq data for automated cell type annotation in single-cell analysis.
A command-line utility that uses convolutional neural networks to search and filter videos based on objects and places that appear in them.
A deprecated Node.js sample application demonstrating IBM Watson Visual Recognition service features.
A reinforcement learning library for Go providing agents, composable tooling, and visualization for solving environment challenges.
A collection of genomic language models for predicting variant effects and evolutionary constraints from DNA sequences.
A minimal implementation of Deep Convolutional Generative Adversarial Networks (DCGAN) using TensorLayerX for generating realistic images.
A deep learning library for streamlining research and development using Torch7 with object-oriented design patterns.
A Python toolbox for content-aware restoration of fluorescence microscopy images using deep learning.
A curated list of resources for understanding, measuring, and mitigating fairness issues in artificial intelligence and machine learning systems.
A JAX library for second-order optimization of neural networks using the K-FAC curvature approximation algorithm.
A lightweight Swift library for tensor calculations with TensorFlow-like APIs, enabling ML model inference.
A Docker-based image annotation tool for bounding box labeling with auto-labeling support, designed for deep learning training.
A deep learning architecture using stacked residual bidirectional LSTM cells with TensorFlow for human activity recognition from sensor data.
GraphDTA predicts drug-target binding affinity using graph neural networks for drug discovery.
Deep learning model using convolutional neural networks to predict drug-target binding affinity from protein sequences and compound SMILES.
A Torch package for creating and visualizing complex neural network architectures using graph-based computation.
A centralized Python framework for agricultural machine learning, providing access to public datasets, benchmarks, pretrained models, and synthetic data generation.
A TensorFlow-based object detection model that localizes and identifies multiple objects in images using SSD MobileNet V1 or Faster R-CNN ResNet101.
An application-oriented Deep Reinforcement Learning framework for real-world decision problems, covering simulation to deployment.
A convolutional neural network for CAPTCHA recognition using Keras and PyTorch.
A JAX library for automatically generating equivariant neural network layers for arbitrary symmetry groups via constraint solving.
OCaml bindings for TensorFlow, enabling machine learning and neural network development in a functional programming environment.
A deep learning-based solution for automatically recognizing and solving 12306 railway website captchas.
An integrated framework for training custom generative AI image-to-image models using GANs, Diffusion, and Consistency Models.
A distributed storage benchmark tool for file systems, object stores, and block devices with GPU support.
A Python library providing comprehensive metrics for fair and thorough evaluation of recommender systems.
A C++ neural network library for Node.js optimized for large datasets and multi-threaded training.
A fast Evolution Strategy implementation in Python
A JAX-based library for federated learning simulations that emphasizes ease-of-use in research.
A PyTorch framework for deep learning on point clouds, providing a modular and reproducible foundation for 3D vision tasks.
A collection of pretrained deep learning models (StyleGAN2, GPT2, VGG, ResNet) for the Jax/Flax ecosystem.
A curated collection of resources on adversarial examples in deep learning, covering attacks, defenses, and applications.
A deep convolutional neural network that predicts RNA-seq coverage at 32bp resolution from DNA sequence.
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